Simple estimators for network sampling (1804.00808v4)

Abstract: A new estimation method is presented for network sampling designs, including Respondent Driven Sampling (RDS) and Snowball (SB) sampling. These types of link-tracing designs are essential for studies of hidden populations, such as people at risk for HIV. The simple idea behind the new method is to run a fast-sampling process on the sample network data to estimate the inclusion probabilities of the actual survey, and incorporate those in unequal probability estimators of population means and proportions. Improved versions of the usual RDS and SB designs are also proposed, termed RDS+ and SB+, to obtain information on more of the within-sample links. In simulations using the network from the Colorado Springs study on the heterosexual spread of HIV, the new estimators produce in most cases lower bias and lower mean square than current methods. For the variables having the largest mean square errors with current estimators, the improvement with the new estimator is dramatic. The estimates are improved even more with the enhanced design versions. For estimating the population mean degree, the efficiency gains using he new method are 29 for RDS, 54 for RDS+, 26 for SB and 80 for SB+. This means for example, with the ordinary RDS design, the mean square error with the new estimator, same data, is 1/29 that of currently used estimators. The new method is computationally intricate but is fast and scales up well. The new estimation method can be used to re-analyze existing network survey data. For new network sampling studies, it is recommended to use the improved designs as well as the new estimators.